lidarwind is an open-source Python project to retrieve wind speed and direction profiles from Doppler lidar observations from the WindCube-200s, and it was developed to be easy to use. It can retrieve wind profiles from the 6-beam and DBS scanning strategies and allow users to set the signal-to-noise ratio threshold to reduce the noise. It also calculates the Reynolds stress tensor matrix elements from the 6-beam observations. lidarwind is a result of an effort to create an environment where it would be flexible and easy to process the observations from the WindCube Doppler lidar.

This paper introduces an experimental setup for retrieving horizontal wind speed and direction profiles with a high temporal and vertical resolution for process studies and validation of convection-permitting model simulations. The CMTRACE (tracing convective momentum transport in complex cloudy atmospheres) campaign used collocated wind lidar and cloud radar measurements to retrieve seamless wind profiles from near the surface up to cloud tops. It took place in Cabauw, the Netherlands, between 13 September and 3 October 2021. The intermediate processing steps for generating the level 1 and level 2 data, such as second trip echoes filtering, offset correction, wind retrieval, re-gridding, and flagging, are described. In level 1 (https://doi.org/10.5281/zenodo.6926483, Dias Neto, 2022a), the data from lidar and radars are kept in the original spatial and temporal resolution, while in level 2 (https://doi.org/10.5281/zenodo.6926605, Dias Neto, 2022b), they are regridded to a common spatial and temporal resolution. Statistical analyses of the lidar's and radar's wind speed and direction profiles indicate a correlation higher than 0.95 for both variables. The bias of wind direction and speed calculated between radar's and lidar's observations are 0.24∘ and −0.16 m s−1, respectively. The foreseen initial application of the datasets includes the study of convective momentum transport and its validation in regional weather forecasts and large-eddy simulation hindcasts.

The horizontal resolution of weather models is increasing, which demands a careful consideration of momentum and energy transport carried across different scales. Deep convective transport and even shallow convective transport, which were previously parameterized, are now resolved, while turbulence remains parameterized. Can observations help constrain the transport by turbulence, coherent structures associated with convection and mesoscale circulations coupled to organized cloud systems?

Using a novel experimental setup for deriving high-resolution continuous wind profiles across the boundary layer, our objective is to provide a fresh view on the variability in horizontal and vertical wind in the presence of a range of (shallow) cloud systems over land, as well as to derive the wind variance and momentum fluxes, and a quantitative assessment of the relevant scales.

The experimental dataset is collected during the Tracing Convective Momentum Transport in Complex Cloudy Atmospheres experiment (CMTRACE). The field campaign occurred at the experimental Cabauw site (The Netherlands) between 13.09.2021 and 03.10.2021 and between 16.05.2022 and 13.06.2022. For this experiment, a cloud radar and wind lidar were operated at 75 degrees elevation, providing horizontal and vertical wind observations within and below the cloud layer. The combined lidar-radar's wind profiles have a vertical resolution of 50 m and a temporal resolution of ~1.5 minutes (which is representative of a horizontal scale of 450 - 1800 m). During CMTRACE, a large variety of cloud regimes were sampled, from non-precipitating shallow convection to deep convective clouds and stratiform clouds.

The observations reveal both coherent thermals (up and downdrafts) and mesoscale divergence and convergence patterns. The scale growth of horizontal momentum variance and flux is well explained by vertical velocity variance, whereby larger vertical velocity variance corresponds to a larger contribution of scales < 35 km to total momentum variance and flux. Additionally, precipitation is shown to separate days on which larger mesoscales contribute to horizontal momentum flux, and the presence of larger cloud structures is confirmed using cloud spatial statistics as viewed from satellite. In an outlook, the representation of these different flows in large-eddy simulation and regional weather hindcasts from the Dutch weather model HARMONIE is evaluated.

The dendritic growth layer (DGL), defined as the temperature region between -20 and -10 °C, plays an important role for ice depositional growth, aggregation and potentially secondary ice processes. The DGL has been found in the past to exhibit specific observational signatures in polarimetric and vertically pointing radar observations. However, consistent conclusions about their physical interpretation have often not been reached. In this study, we exploit a unique 3-months dataset of mid-latitude winter clouds observed with vertically pointing triple-frequency (X-, Ka-, W-band) and polarimetric W-band Doppler radars. In addition to standard radar moments, we also analyse the multi-wavelength and polarimetric Doppler spectra. New variables, such as the maximum of the spectral differential reflectivity (ZDR) (sZDRmax), allows us to analyse the ZDR signal of asymmetric ice particles independent of the presence of low ZDR producing aggregates. This unique dataset enables us to investigate correlations between enhanced aggregation and evolution of small ice particles in the DGL. For this, the multi-frequency observations are used to classify all profiles according to their maximum average aggregate size within the DGL. The strong correlation between aggregate class and specific differential phase shift (KDP) confirms the expected link between ice particle concentration and aggregation. Interestingly, no correlation between aggregation class and sZDRmax is visible. This indicates that aggregation is rather independent of the aspect ratio and density of ice crystals. A distinct reduction of mean Doppler velocity in the DGL is found to be strongest for cases with largest aggregate sizes. Analyses of spectral edge velocities suggest that the reduction is the combined result of the formation of new ice particles with low fall velocity and a weak updraft. It appears most likely that this updraft is the result of latent heat released by enhanced depositional growth. Clearly, the strongest correlations of aggregate class with other variables are found inside the DGL. Surprisingly, no correlation between aggregate class and concentration or aspect ratio of particles falling from above into the DGL could be found. Only a weak correlation between the mean particle size falling into the DGL and maximum aggregate size within the DGL is apparent. In addition to the correlation analysis, the dataset also allows study of the evolution of radar variables as a function of temperature. We find the ice particle concentration continuously increasing from -18 °C towards the bottom of the DGL. Aggregation increases more rapidly from -15 °C towards warmer temperatures. Surprisingly, KDP and sZDRmax are not reduced by the intensifying aggregation below -15 °C but rather reach their maximum values in the lower half of the DGL. Also below the DGL, KDP and sZDRmax remain enhanced until -4 °C. Only there, additional aggregation appears to deplete ice crystals and therefore reduce KDP and sZDRmax. The simultaneous increase of aggregation and particle concentration inside the DGL necessitates a source mechanism for new ice crystals. As primary ice nucleation is expected to decrease towards warmer temperatures, secondary ice processes are a likely explanation for the increase in ice particle concentration. Previous laboratory experiments strongly point towards ice collisional fragmentation as a possible mechanism for new particle generation. The presence of an updraft in the temperature region of maximum depositional growth might also suggest an important positive feedback mechanism between ice microphysics and dynamics which might further enhance ice particle growth in the DGL.

Comparing the reflectivity flux at the top and bottom of the melting layer (ML) reveals the overall effect of the microphysical processes occurring within the ML on the particle population. If melting is the only process taking place and all particles scatter in the Rayleigh regime, the reflectivity flux increases in the ML by a constant factor given by the ratio of the dielectric factors. Deviations from this constant factor can indicate that either growth or shrinking processes (breakup, sublimation, and evaporation) dominate. However, inference of growth or shrinking dominance from the increase in reflectivity flux is only possible if other influences (e.g., vertical wind speed) are negligible or corrected. By analyzing radar Doppler spectra and multi-frequency observations, we correct the reflectivity fluxes for vertical wind and categorize the height profiles by the riming degree at the ML top. We apply this reflectivity flux ratio (ZFR) approach to a multi-month mid-latitude winter data set that contains mostly stratiform clouds. The profiles of radar variables in the ML are found to be surprisingly similar for both unrimed and rimed profiles with slight differences, for example, in the absolute values of the reflectivity flux. Statistical analysis of the ZFR suggests that either microphysical processes other than melting are not important or strongly compensate for each other. The results seem to confirm that at least for moderately precipitating stratiform clouds, the melting-only assumption applied in several retrievals and microphysical schemes is reasonable.

Convective clouds may be associated with substantial transport of momentum. Much of what we know about convective momentum transport stems from high-resolution simulations because high-resolution measurements of the wind profile are rare. This study exploits ground-based remote sensing techniques to visualize wind below and within clouds and their surroundings, to assess momentum transport. The Tracing Convective Momentum Transport in Complex Cloudy Atmospheres experiment (CMTRACE) took place at the experimental Cabauw site (The Netherlands) between 13.09.2021 and 03.10.2021. The goal of CMTRACE was to provide continuous profiles of horizontal and vertical wind with a temporal resolution of ~1 minute and vertical resolution of ~50 m within the cloud and sub-cloud layers to improve our understanding of the role of momentum transport from cloud- to mesoscales. A scanning wind lidar provided the observations in the sub-cloud layer, while in the cloud layer, one scanning and one vertically pointing cloud radar provided observations. During CMTRACE, we sampled various cloud regimes including non-precipitating shallow cumulus clouds, deep convective clouds and stratiform clouds. In this study, we illustrate some of the most interesting CMTRACE observations that reveal the circulations (winds) near clouds and present statistical analyses as a function of different cloud regimes. Specifically, we calculate profiles of wind fluctuations and their cross-correlations to address the momentum flux carried on cloud- and mesoscale scales. The observations from different cloud regimes (e.g. clear sky, shallow convection and frontal passage) are compared to momentum fluxes and wind variability in the Dutch Large-Eddy Simulations nested on the experimental site for the selected days.

Convective clouds may be associated with substantial transport of momentum. The process of convective momentum transport is typically investigated using simulations due to a lack of observations. This study exploits the currently available remote sensing techniques to visualize wind structures within clouds and their surroundings and quantify the vertical transport of momentum.

The Tracing Convective Momentum Transport in Complex Cloudy Atmospheres experiment (CMTRACE) took place in the experimental site in Cabauw (The Netherlands) between September 13th and October 3rd 2021, as part of the RUISDAEL project. The goal of CMTRACE was to provide continuous profiles of horizontal and vertical wind components with a temporal resolution of ~1 minute and vertical resolution of ~50 m within the cloud and sub-cloud layers to improve our understanding of the role of momentum transport on different scales. One scanning wind lidar provided the observations in the sub-cloud layer, while in the cloud layer, the observations were obtained by one scanning and one vertically pointing cloud radar. The high-resolution data produced by those instruments across the boundary layer can also benefit data assimilation and model evaluation.

During CMTRACE, we sampled various cloud regimes such as non-precipitating shallow cumulus, deep convective clouds and stratiform clouds. Due to the presence of insects, the radar provided almost identical wind profiles to the lidar up to cloud base, giving us confidence in the quality of the observations. The dataset was also validated against the data from radiosondes and the Cabauw mast tower.

In this presentation, we outline the CMTRACE observational dataset and present statistical analyses and classification of the data into different cloud regimes. The profiles of wind fluctuations and momentum fluxes are used to exemplify correlations between vertical and horizontal wind on both cloud- and mesoscale scales.

Understanding which microphysical processes are dominant while ice particles pass through the melting layer is essential for precipitation prediction by microphysics schemes and precipitation estimates by remote sensing. Comparing the reflectivity flux at the top and bottom of the melting layer reveals the overall effect of the microphysical processes occurring within the melting layer on the particle population. If the reflectivity flux increases more than expected due to the change in the dielectric factor, growth processes dominate. In contrast, a weaker increase in reflectivity flux indicates that shrinking processes dominate. However, inference of growth or shrinking dominance from the increase in reflectivity flux is only possible if other influences (e.g., vertical wind speed) are negligible or corrected for. By analyzing radar spectra and multi-frequency observations, we correct the reflectivity fluxes for vertical wind speed and categorize the height profiles by the riming degree at the melting layer top. Our statistical analysis shows the slight dominance of growth processes for unrimed and a clearer dominance of shrinking processes for rimed profiles. The reflectivity flux profiles within the melting layer indicate that the difference between unrimed and rimed profiles arises mainly in the upper half of the melting layer, where the melting fraction increases the strongest. We further narrow down which processes might be most important to explain the observed signature by analyzing additional radar variables. We suggest that whether the particle population is overall growing or shrinking depends on the relative importance of aggregation and collisional breakup of melting particles.

Vertically pointing radar observations combining multiple frequencies and Doppler measurements have been recently shown to contain valuable information about ice particle growth processes, such as aggregation and riming. In this study, we use a two-months X, Ka, W-Band Doppler radar dataset of midlatitude winter clouds to infer statistical growth signatures of ice and snow particles. The observational statistics are compared to forward-simulated radar moments based on simulations of the campaign time period with a high-resolution version of the ICON model and a two-moment microphysical scheme. The statistical comparison shows very good agreement of the simulated vertical structure of radar reflectivity and surface precipitation rate. The dual-wavelength ratios, which are closely related to the mean particle size, also show consistently a major increase at temperatures higher than –15 °C. However, at temperatures higher than –7 °C, ICON increasingly overestimates the mean particle size. The statistics of mean Doppler velocities also reveal that the model overestimates the terminal velocity of snow particles, especially at larger sizes. We discuss possible reasons for the identified discrepancies, such as an unrealistic temperature dependence of the sticking efficiency or the non-saturation of terminal velocities at larger sizes caused by the implemented power law relations. Our study demonstrates examples of the importance of combining various radar techniques for identifying issues in simulated microphysical processes, which can otherwise be hidden due to compensating errors.

A retrieval for characteristic raindrop size and width of the drop size distribution (DSD) based on triple-frequency vertical Doppler radar measurements is developed. The algorithm exploits a statistical relation that maps measurements of the differential Doppler velocities at X and Ka and at Ka and Wbands into the two aforementioned DSD moments. The statistical mapping has been founded on 7,900 hr of disdrometer-observed DSDs and their simulated Doppler velocities. Additionally, a retrieval of Dm based only on DDVX−W measurements is also presented, and its performance is compared to the analogous algorithm exploiting DDVKa−W data. The retrievals are tested using triple-frequency radar data collected during a recent field campaign held at the Juelich Observatory for Cloud Evolution (JOYCE, Germany) where in situ measurements of the DSD were carried out only few meters away from the vertically pointing radars. The triple-frequency retrieval is able to obtain Dm with an uncertainty below 25% for Dm ranging from 0.7 to 2.4 mm. Compared to previously published dual-frequency retrievals, the third frequency does not improve the retrieval for small Dm (< 1.4 mm). However, it significantly surpasses the DDVKa−W algorithm for larger Dm (20% versus 50% bias at 2.25 mm). Also compared to DDVX−W method, the triple-frequency retrieval is found to provide an improvement of 15% in terms of bias for Dm = 2.25 mm. The triple-frequency retrieval of 𝜎m performs with an uncertainty of 20–50% for 0.2 < 𝜎m < 1.3 mm, with the best performance for 0.25 < 𝜎m < 0.8 mm.

This paper describes a 2-month dataset of ground-based triple-frequency (X, Ka, and W band) Doppler radar observations during the winter season obtained at the Jülich ObservatorY for Cloud Evolution Core Facility (JOYCE-CF), Germany. All relevant post-processing steps, such as re-gridding and offset and attenuation correction, as well as quality flagging, are described. The dataset contains all necessary information required to recover data at intermediate processing steps for user-specific applications and corrections (https://doi.org/10.5281/zenodo.1341389; Dias Neto et al., 2019). The large number of ice clouds included in the dataset allows for a first statistical analysis of their multifrequency radar signatures. The reflectivity differences quantified by dual-wavelength ratios (DWRs) reveal temperature regimes where aggregation seems to be triggered. Overall, the aggregation signatures found in the triple-frequency space agree with and corroborate conclusions from previous studies. The combination of DWRs with mean Doppler velocity and linear depolarization ratio enables us to distinguish signatures of rimed particles and melting snowflakes. The riming signatures in the DWRs agree well with results found in previous triple-frequency studies. Close to the melting layer, however, we find very large DWRs (up to 20 dB), which have not been reported before. A combined analysis of these extreme DWR with mean Doppler velocity and a linear depolarization ratio allows this signature to be separated, which is most likely related to strong aggregation, from the triple-frequency characteristics of melting particles.

The workshop gathered almost 50 scientists from Europe and the United States to discuss the progress toward developing electromagnetic scattering databases for ice and snow particles in the microwave region, their applications, the physical approximations used to compute these scattering properties, and how remote sensing and in situ observations can be used to validate scattering datasets.

Resumo

Uma metodologia para corrigir observações de Radiação de Onda Longa Descendente (OLD) geradas por estações de superfície é apresentada. Consiste na aplicação do método de mínimos quadrados para encontrar coeficientes (ks) específicos de sensores PIR Eppley (EP) e CGR4 Kipp & Zonen (KZ) e reprocessamento das séries temporais. A avaliação da metodologia utilizou dados obtidos de um experimento realizado no Laboratório de Instrumentação Meteorológica - CPTEC/INPE. A aplicação de testes estatísticos na série de dados, com e sem correção, mostra uma redução significativa da Raiz do Erro Quadrático Médio, que passou de 9,02 a 0,43 W/m2 para o sensor EP e de 4,11 a 1,21 W/m2 para o KZ; o viés reduziu de −7,99 para 0,50 W/m2 (EP) e de 3,53 para 0,27 W/m2 (KZ). Um sensor CG4 (KZ) de referência calibrado no Physikalisch-Meteorologiches Observatorium Davos (PMOD), com rastreabilidade foi utilizado.

Abstract

Hereby is presented a methodology to correct observations of Downwelling Longwave Radiation (DLR) generated by surface stations. It consists in the application of the least squares method to find coefficients (ks) of PIR Eppley (EP) and CGR4 Kipp & Zonen (KZ) sensors, and reprocessing of time series. The evaluation of the methodology used data obtained in an experiment conducted in the Meteorological Instrumentation Laboratory - CPTEC / INPE. The application of statistical tests in the data series, with and without correction shows a significant reduction of the Root Mean Square Error, which went from 9,02 to 0,43 W/m2 for the EP sensor and 4,11 to 1,21 W / m2 for KZ; bias decreased from 0,50 to −7,99 W/m2 (EP) and 3,53 to 0,27 W/m2 (KZ). A CG4 sensor (KZ) of the calibrated reference Physikalisch-Meteorologiches Observatorium Davos (PMOD) with traceability was used.